FPGA Implementation of SVM Decision Function Based on Hardware-Friendly Kernel

We present a design scheme for SVM decision function based on the hardware-friendly kernel on FPGA device. This scheme is suitable for classification and regression problems. We adopt ModelSim simulation platform for SVM classification and regression experiments. The hardware implementation obtains the same classification accuracy as the LIBSVM package by using the appropriate fixed-point number precision in classification experiments. We had done the preliminary study on the precision of input parameters in SVC by choosing fixed-point arithmetic; and the minimum number of bits of SVR input parameters was obtained in the case of not reducing the performance of SVM classifier. The mean square error of the hardware implementation is less than 0.004 in regression experiments, with good regression performance.